Temporal Dynamics Modeling

Temporal Dynamics Modeling is a computational framework within sequential and session-based recommendation that explicitly captures and represents the time-varying patterns and evolution of user-item interactions over time to improve predictive accuracy.

143

Publications

9.7K

Citations

470

Authors

182

Institutions

Publications per year

2017–2026

88

Authors

470

  1. JL
    Jure Leskovec

    Stanford University, United States

    3 publications · 70 citations

    3

    h-index

  2. KR
    Kira Radinsky

    Technion – Israel Institute of Technology, Israel

    3 publications · 202 citations

    3

    h-index

  3. JZ
    Jie Zhang

    Nanyang Technological University, Singapore

    3 publications · 142 citations

    3

    h-index

  4. SD
    Susan Dumais

    Microsoft (United States), United States

    3 publications · 202 citations

    3

    h-index

  5. TM
    Tao Mei

    Microsoft Research Asia (China), China

    2 publications · 126 citations

    2

    h-index

1–5 of 470

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 215 citations

    10

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 1.2K citations

    6

    h-index

  3. ACM Transactions on Information Systems

    4 publications · 239 citations

    4

    h-index

  4. Knowledge-Based Systems

    3 publications · 50 citations

    3

    h-index

  5. IEEE Access

    3 publications · 42 citations

    3

    h-index

1–5